Landisch and Zahler

Quantifying Land Cover Change

And Urban Development

In Rochester, MN

FR 3262

Patrick Landisch and Stephanie Zahler

5/10/13

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Introduction

Humans have had an incredible impact on the environment over the course of our

existence. Over recent years, our population has continued to grow exponentially and this has

caused major changes in land use and land cover. With more than 80% of the world’s

population living in urban areas, studying the growth and development of cities is critical in

assessing land cover change (Veldkamp and Verburg 2004). It is essential to study the effects of

urbanization and understand the various implications a changing land cover can have on the

environment such as the loss of forest and agricultural fields, increased runoff due to

impervious surfaces, CO2 emissions from urban areas etc. There are many more environmental

concerns out there and awareness and action are necessary, but so is urban development.

People need a place to live, but we need to consider Earth, our true home, as we change it in

developing more living space.

For our project we decided to monitor urban development in Olmsted County. The city

of Rochester is located in this county and is the third largest city in Minnesota. From 1980 to

2000 the city saw a rise in population from 57,890 to 70,745 (U.S. Census Bureau). In order to

accurately study the land cover change in this area, we used data downloaded from the

internet and the ERDAS Imagine software program to assess urban development from 1985 to

2001.

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Methodology

-Defining the study area-

Olmsted County has a total of 654.50 square miles of land and water. In order to do any

analysis of Rochester, we first needed to define an exact location as our area of interest. After

examining the Public Land Survey (PLS) township boundaries, we concluded that a total of four

townships will be sufficient enough for the scope of this project: Cascade, Haverhill, Marion,

and Rochester. This amounted to a total of 106.7 square miles. These four counties encompass

the city of Rochester as well as the surrounding area that may or not be subjected to urban

development and land use change.

-Data acquisition-

To obtain the necessary data for this project, we used two sites to gather the

preliminary resources. First, we accessed the MN Data Deli and downloaded the PLS township

boundary shapefile. Second, through the United States Geologic Survey Glovis website, we

were able to select Landsat 5 satellite images of our study area. We selected images from the

spring of 1985 and 2001. However, we were not able to find images from the same month so

we settled for April and May. To eliminate cloud cover, we used a 10% cloud cover filter on

both of the images in hopes that it would negate any obstruction that clouds may present. Both

images were downloaded and imported to ERDAS Imagine for further image processing

-Image Preprocessing-

In order to successfully import Landsat images, it is necessary to use the Layer Stack tool

in ERDAS. This step requires the user to stack the .tif files on top of each other, creating a final

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output image. Once this was successfully done the next step was to georeference each image

so that they were both projected in the same reference system. We used the UTM NAD83 Zone

15N projection and datum for our images.

Meanwhile, we imported the PLS township boundary data into ArcMap to define our

study area. By opening the attribute table and selecting the appropriate townships, we

highlighted our study area. In order to use this file in ERDAS, we exported the selected features

to a USB flash drive as a shapefile and were then able to import it to ERDAS.

Within the same 2D Viewer of ERDAS Image, we opened both our Landsat satellite

raster layer and our four township vector layer. To cut out our study area from the satellite

images, we needed to perform a simple clip operation. The first step in doing this was to open a

new Area of Interest file. By clicking on each township and copying it, we were able to paste it

to our Area of Interest file. The clip tool allowed us to essentially cut out our study area from

the Landsat image. After doing this for both of our satellite images, we were ready for the

classification step.

-Image Classification-

We chose to perform a supervised classification on both of our images. It was agreed

upon that through a supervised classification, we would be able to more accurately select our

training areas. We decided that a total of 5 classes would be used for this classification: Urban,

Water, Bare Soil/Vegetation, Green Vegetation, and Forest. The choice to separate bare

soil/vegetation and green vegetation was in hopes that it would make the identification,

classification and quantification easier. We selected 10 to 20 training areas for each class. After

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each training area was created, it would be added to the Signature Editor tool. Finally, with all

the training areas added we then merged them for each defined class. After which we were

able to run the classification. The distinction between agriculture and forest was fairly easy to

make in that forested areas had a courser texture, irregular shape, and a darker shade of red

than the brighter color and uniform shape of agricultural fields. Urban areas were easy to

detect, however some areas appeared to have mixed pixels, which we believed were suburban

areas.

-Change Detection-

After our classifications were complete, it was necessary to quantify the amount of

change that happened from 1985 to 2001. In order to observe this change quantitatively, we

performed a thematic change detection on both the images. With the thematic change we

were able to also create a Summary Report. This gave us the amount of land changed from

each zone throughout the 16 year period and to what class it was converted to. This is the

advantage of a thematic change detection, it gives the user “from-to” information about the

two images. With this information we were able to quantify how much land from water,

vegetation, and forest was converted to urban land over the span of 16 years.

-Accuracy Assessment-

We performed an accuracy assessment on the 2001 image. To do so, we downloaded

NAIP imagery of the state of Minnesota from the USDA NRCS website. We applied the same

image processing steps to slim down the image to just our study area. After doing so, this image

was used as our reference data for the accuracy assessment. However, it is important to note

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that the earliest land cover image provided from the NAIP imagery set was 2006. This might

cause some error in accuracy, but it was the best reference data we were able to find. A

stratified random sample method was used with 105 total points created and a minimum of 15

for each class. The error matrix shows our 2001 image had an accuracy of 71% and a Kappa

statistic of 0.64. These numbers are fairly close, so we feel confident in our assessment. We

believe the majority of error is due to our attempt to distinguish between green vegetation and

bare soil.

Results (all information can be found in the Appendix)

After the completion of all these steps it is clear that there has been an increase in

urban development. With the increase in urban space comes a decrease in land cover and land

use for forests and agriculture. Almost 24% of the classified vegetation was converted to urban

land. This amounted to a total of 3,685.58 acres converted from agricultural to urban

development. As for forestland converted to urban, 15% had been converted to urban which

amounted to 4,829.1 acres of land. As one can see from the images in our Appendix, the urban

growth is most prominent in the northwest and south central region of images. The central area

of Rochester also has a clear rise in urban development, most likely due to the continual

development of urban infrastructure such as commercial buildings, new roads, housing etc.

Discussion

It is comes to no surprise that the city of Rochester has experienced an increase in urban

development. This may be directly correlated to the rise in population from 1980 to 2000. The

population of 70,745 in 2000 in comparison to 57,890 in 1980 explains the significant increase

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in urban land cover and the need to convert forest and agricultural land to urban areas. One of

the drawbacks to our project was the fact that we did not use Landsat 7 information from 2012.

Due to the scan lines in the Landsat 7 imagery, it would have been a challenging task to

accurately and efficiently classify and detect land cover change. However, this project would

have been more relevant. Another thing we would have done differently would be to classify

agriculture as one class instead of bare soil/vegetation and green vegetation. Another setback

to our project was the absence of a reference map for the 1985 classification image. This

prevented us from successfully performing an accuracy assessment on the 1985 image.

Land cover change poses a variety of environmental issues that require attention and

mitigation plans. An increase in impervious surfaces due to urban development may lead to an

increase in soil erosion, water pollution, C02 emissions etc. Although our objective was not to

study the environmental impacts of urban development, studying and knowing which areas

were converted to urban surfaces is important in identifying starting points for mitigation plans.

Overall, we feel confident in the work we accomplished for this project. In retrospect,

there are a couple of things we would have differently. However, for the first time using ERDAS

Imagine to complete a project with satellite imagery, we believe that we have provided a

detailed procedure for studying land cover and land use change. This was a learning experience

working with ERDAS, and upon completion we feel that we have stronger and deeper

understanding of the process of image classification and applying satellite imagery to real life

situations

Appendix

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Supervised Classification of 1985 Image

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Supervised Classification of 2001 Image

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2006 NAIP Reference Image of MN Land Cover

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Percent Change Chart

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Percent Change to Urban Area 1985-

2001

Percent Change Acres

Bare Agriculture to Urban 0.1367 3119.48

Water to Urban 0.1998 37.42

Green Vegetation to Urban 0.1085 566.1

Forest to Urban 0.1513 4829.1

Urban to Urban (Acres in 1985) 15503

Total 0.5963 24055.1

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Accuracy Assessment for 2001 Supervised Classification

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References

Veldkamp, A., and P. H. Verburg. "Modelling land use change and environmental impact." Journal of

Environmental Management 72.1 (2004): 1-3.

http://www.census.gov/

FR 3262 Lab Manual 7 Image Operations and Clip

FR 3262 Lab Manual 11 & 11a Supervised Classification and Accuracy Assessment

FR 3262 Lab Manual 12 Change Detection

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